Utility of Neurophysiological Evaluation in Movement Disorders Clinical Practice
Bibliographic record
Abstract
Background: Quantitative and objective neurophysiological assessment can help to define the predominant phenomenology and provide diagnoses that have prognostic and therapeutic implications for movement disorders. Objectives: Evaluate the agreement between initial indications and final diagnoses after neurophysiological evaluations in a specialized movement disorders center. Methods: Electrophysiological studies conducted for movement disorders from 2003 to 2021 were reviewed. The indications were classified according to predominant phenomenology and the diagnoses categorized in subgroups of phenomenology. Results: A total of 509 studies were analyzed. 51% (259) of patients were female, with a mean age of 51 years (ranges 5 to 89 years). The most common reasons for referral were evaluation of functional movement disorders (FMD), followed by jerky movements, tremor and postural instability. Regarding FMD referrals, there was a diagnostic change in 13% of the patients after electrophysiological assessment. The patients with jerky movements as indication had a diagnosis other than myoclonus in 27% of them, and tremor was not confirmed in 20% of the cases. In patients with an electrophysiological diagnosis of FMD, it was not suspected in 30% of the referrals. Similarly, tremor was not mentioned in the referral of 17% of the patients with this electrophysiological diagnosis and myoclonus was not suspected in 13% of the cases. Conclusions: Electrophysiological assessment has utility in the evaluation of movement disorders, even in patients evaluated by movement disorders neurologists. More studies are needed to standardize the protocols between centers and to promote the availability and use of these techniques among movement disorders clinics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".